Google Earth Engine Tutorial: Monitor Vegetation Moisture Dynamics

Google Earth Engine Tutorial: Monitor Vegetation Moisture Dynamics

Google Earth Engine (GEE) is a powerful platform for analyzing geospatial data, enabling users to monitor environmental changes at scale. This tutorial focuses on using GEE to track vegetation moisture dynamics, which is critical for agriculture, ecological studies, and climate research. By combining remote sensing datasets with GEE’s processing capabilities, you can derive insights into plant health and water availability over time.

Getting Started

To begin, ensure you have access to a GEE account. Navigate to the Google Earth Engine Code Editor and create a new script. This environment allows you to write and run JavaScript code, leveraging GEE’s APIs to process satellite imagery and datasets.

Step 1: Load Vegetation and Moisture Datasets

GEE provides numerous datasets for vegetation and moisture analysis. For vegetation, the MODIS/006/MOD09GA dataset (EVI) is commonly used. For moisture, NASA/HLS/HLSL30/v002 (HLS) or ESA/CCI/CLIMAT/1_0 (soil moisture) can be relevant:


// Load MODIS EVI dataset
var evi = ee.ImageCollection("MODIS/006/MOD09GA").select('EVI');

// Load soil moisture dataset
var soilMoisture = ee.ImageCollection("NASA/HLS/HLSL30/v002").select('soil_moisture');


Step 2: Preprocess Data

Preprocessing is essential to remove noise and ensure accuracy. For example, filter clouds in the EVI dataset and adjust spatial resolution:


// Filter images by date and location
var filteredEVI = evi.filterDate('2020-01-01', '2020-12-31')
.filter(ee.Filter.geometry(ee.Geometry.Rectangle([long1, lat1, long2, lat2])));

// Apply cloud masking (example function)
var cloudFreeEVI = filteredEVI.map(function(image) {
return image.updateMask(image.select('internalCloudAlgorithmShifted').lt(0.05));
});


Step 3: Calculate Vegetation Moisture Index

Vegetation moisture is often derived using indices like the Enhanced Vegetation Index (EVI). Combine this with soil moisture data for a comprehensive analysis:


// Calculate EVI and soil moisture
var eviCollection = cloudFreeEVI.map(function(image) {
return image.select('EVI').rename('veg_evi');
});

var moistureCollection = soilMoisture.map(function(image) {
return image.select('soil_moisture').rename('soil_moisture');
});

// Merge datasets
var merged = ee.ImageCollection(eviCollection).merge(moistureCollection);


Step 4: Visualize and Analyze

Use GEE’s ui.Map to visualize data and ui.Chart to plot trends over time. For example, display EVI and soil moisture on the same map:


// Create a map and add layers
var map = ui.Map();
map.addLayer(evi.first(), {min: 0, max: 1}, 'EVI');
map.addLayer(soilMoisture.first(), {min: 0, max: 1}, 'Soil Moisture');
map.centerObject(ee.Geometry.Rectangle([long1, lat1, long2, lat2]), 10);

// Plot vegetation and moisture trends
var chart = ui.Chart.image.series({
imageCollection: merged.select(['veg_evi', 'soil_moisture']),
region: ee.Geometry.Rectangle([long1, lat1, long2, lat2]),
reducer: ee.Reducer.mean(),
xProperty: 'system:time_start'
}).setChartType('LineChart');

print(chart);


Step 5: Export Results

Once visualized, export data for further use. For instance, export a time series of EVI values as a CSV file:


// Export a time series of EVI data
var exportTask = Export.image.toDrive({
image: merged.select('veg_evi'),
description: 'veg_evi_timeseries',
folder: 'GEE_exports',
scale: 30,
fileNamePrefix: 'veg_evi',
format: 'CSV'
});


Conclusion

By integrating datasets and leveraging GEE’s tools, you can efficiently track vegetation moisture dynamics. This approach aids in identifying trends, anomalies, and patterns that inform resource management decisions.

FAQ

What datasets are best for vegetation moisture analysis?

MODIS EVI for vegetation and NASA HLS or ESA CCI soil moisture datasets for moisture. Choose based on your study area and resolution needs.

How to handle cloud cover in satellite data?

Use the filter method with internalCloudAlgorithmShifted or similar cloud masking functions provided by the dataset.

Can I analyze data for a specific time range?

Yes, use filterDate() to set your desired date range, such as ‘2020-01-01’ to ‘2020-12-31’.

How to add custom datasets to GEE analysis?

Upload the data as a GeoTIFF or other supported format, then use ee.Image.loadGeoJson() or similar functions.

Is there a cost associated with using GEE?

Basic access is free, but advanced computations or large-scale exports may require a GEE Premium subscription.